182 citations · 616 across the 20 of their papers we have counts for
5 papers · 1 filter
Learning Hidden Dynamics using Intelligent Automatic Differentiation
Kailai Xu, Dongzhuo Li, Eric Darve +1
Many engineering problems involve learning hidden dynamics from indirect observations, where the physical processes are described by systems of partial differential equations (PDE)…
Coupled Time-lapse Full Waveform Inversion for Subsurface Flow Problems using Intrusive Automatic Differentiation
Dongzhuo Li, Kailai Xu, Jerry M. Harris +1
We describe a novel framework for estimating subsurface properties, such as rock permeability and porosity, from time-lapse observed seismic data by coupling full-waveform inversio…
Adversarial Numerical Analysis for Inverse Problems
Kailai Xu, Eric Darve
Many scientific and engineering applications are formulated as inverse problems associated with stochastic models. In such cases the unknown quantities are distributions. The appli…
Learning Constitutive Relations from Indirect Observations Using Deep Neural Networks
Daniel Z. Huang, Kailai Xu, Charbel Farhat +1
We present a new approach for predictive modeling and its uncertainty quantification for mechanical systems, where coarse-grained models such as constitutive relations are derived…
The Neural Network Approach to Inverse Problems in Differential Equations
Kailai Xu, Eric Darve
We proposed a framework for solving inverse problems in differential equations based on neural networks and automatic differentiation. Neural networks are used to approximate hidde…